Quantum Many-Body Dynamics Group

This page contains the complete list of publications by members of the group¹.

Ozgur Sahin, Hawraa Al Asadi, Paul Schindler, Arjun Pillai, Erica Sanchez, Mark Elo, Maxwell McAllister, Emanuel Druga, Christoph Fleckenstein, Marin Bukov, Ashok Ajoy,
Continuously tracked, stable, large excursion trajectories of dipolar coupled nuclear spins,
arXiv:2206.14945.
Jiahao Yao, Haoya Li, Marin Bukov, Lin Lin, Lexing Ying,
Monte Carlo Tree Search based Hybrid Optimization of Variational Quantum Circuits,
arXiv:2203.16707. (accepted for publication in Mathematical and Scientific Machine 2022)
Friederike Metz and Marin Bukov,
Self-Correcting Quantum Many-Body Control using Reinforcement Learning with Tensor Networks,
arXiv:2201.11790.
William Beatrez, Christoph Fleckenstein, Arjun Pillai, Erica Sanchez, Amala Akkiraju, Jesus Alcala, Sophie Conti, Paul Reshetikhin, Emanuel Druga, Marin Bukov, and Ashok Ajoy,
Observation of a long-lived prethermal discrete time crystal created by two-frequency driving,
arXiv:2201.02162.

Christoph Fleckenstein and Marin Bukov,
Thermalization and Prethermalization in Periodically Kicked Quantum Spin Chains,
Phys. Rev. B 103, 144307 (2021).

Christoph Fleckenstein and Marin Bukov,
Prethermalization and Thermalization in Periodically-Driven Many-Body Systems away from the High-Frequency Limit,
Phys. Rev. B 103, L140302 (2021).
Jiahao Yao, Paul Köttering, Hans Gundlach, Lin Lin, and Marin Bukov,
Noise-Robust End-to-End Quantum Control using Deep Autoregressive Policy Networks,
PMLR 107:1–36, 2021 2nd Annual Conference on Mathematical and Scientific Machine Learning.
Marin Bukov, Markus Schmitt, and Maxime Dupont,
Learning the ground state of a non-stoquastic quantum Hamiltonian in a rugged neural network landscape,
SciPost Phys. 10, 147 (2021).
Jiahao Yao, Lin Lin, and Marin Bukov,
Reinforcement Learning for Many-Body Ground State Preparation based on Counter-Diabatic Driving,
Phys. Rev. X 11, 031070 (2021).